The time series recorded by each data-logger reflects the evolution of the measured parameter versus time, and it is commonly denoted as trajectory.
As all trajectories are quite parallel, the average registered during the calibration period by each data-logger is a representative value.
Average temperature and relative humidity were then calculated from data recorded by each data-logger, daily, at 3 am and at 6-hour intervals, throughout the monitoring period.
For the first period, the bias was calculated as the mean temperature recorded by each data-logger minus the average from all of them, which was regarded as the exact value.
Firstly, we computed the mean values recorded in 2010 by each data-logger at 0 00, 0 30, 1 00… and so on until 12 00 PM, resulting a daily mean trajectory.
If RH data from #2 are disregarded for the calibration study, it turns out that the correlation between the average RH recorded by each data-logger in the first vs. the second calibration period is statistically significant (r = 0.701, p = 0.0001).
Among the different possible methods of data treatment, the Box-Whiskers graphs were chosen since they provide the main information on the differences recorded throughout the monitoring period by each data-logger, i.e. median values, 5°, 10°, 25°, 90°, 95° percentiles and spreads.
Data transmitted from wireless body sensor can be captured either by Nordic or by Bluetooth in the data logger and then can be uploaded wirelessly to personal computer or Smartphone using Bluetooth.
The output electric energy is recorded by the data logger throughout the process.
The test data were captured by a data logger connected to all the permeameters.
Online measured data, such as biogas production, temperature, and pH, were recorded by a data logger.
